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Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice

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  • Yeon-Koo Che
  • Dong Woo Hahm
  • YingHua He

Abstract

Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the mistakes in a deferred-acceptance matching environment. The key insight is that the uncertainties faced by applicants, e.g., due to tie-breaking lotteries, render some mistakes costly, allowing us to reliably infer relevant preferences. Our approach extracts all information on preferences robustly to payoff-insignificant mistakes. We apply it to school-choice data from Staten Island, NYC. Counterfactual analysis suggests that we underestimate the effects of proposed desegregation reforms when applicants' mistakes are not accounted for in preference inference and estimation.

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  • Yeon-Koo Che & Dong Woo Hahm & YingHua He, 2023. "Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice," Papers 2309.14297, arXiv.org.
    • Handle: RePEc:arx:papers:2309.14297
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    References listed on IDEAS

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    1. Mariana Laverde, 2022. "Distance to Schools and Equal Access in School Choice Systems," Boston College Working Papers in Economics 1046, Boston College Department of Economics, revised 15 Jun 2024.
    2. Mariana Laverde, 2022. "Distance to Schools and Equal Access in School Choice Systems," Working Papers 2022-002, Human Capital and Economic Opportunity Working Group.
    3. Nikhil Agarwal & Paulo Somaini, 2018. "Demand Analysis Using Strategic Reports: An Application to a School Choice Mechanism," Econometrica, Econometric Society, vol. 86(2), pages 391-444, March.
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